RTX 3060 12GBコスパ重視12 GB VRAM (+4)360 GB/s (+104)
B
Raises estimated decode speed by about 32%.50.2 tok/sデコード
Raises estimated decode speed by about 32%.
Adds memory headroom for longer context windows and future model growth.
〜$329 MSRP
Can Mistral 7B Instruct v0.3 run on GTX 1070 8GB?
YES — With Offload
B63Good
Estimated from fit model
Mistral 7B Instruct v0.3 needs ~7.9 GB VRAM. GTX 1070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~38 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 7.9 GB, 38.0 tok/s, Runs with offload
7.9 GB required8.0 GB available
99% VRAM used
Fit status
Runs with offload
Decode
38.0 tok/s
TTFT
5091 ms
Safe context
8K
Memory
7.9 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsMistral 7B Instruct v0.3 on GTX 1070 8GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 38.0 tok/s decode · 5.1s TTFT (warm) · 95 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 38.0 tok/s | 2777 ms | 8K |
| Coding | B | Runs with offload | 38.0 tok/s | 5091 ms | 8K |
| Agentic Coding | F | Too heavy | 17.5 tok/s | 16126 ms | 8K |
| Reasoning | B | Runs with offload | 38.0 tok/s | 6017 ms | 8K |
| RAG | F | Too heavy | 17.5 tok/s | 20157 ms | 8K |
Quantization options
How Mistral 7B Instruct v0.3 (7B params) fits at each quantization level on GTX 1070 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B65 |
Q3_K_S | 3 | 3.4 GB | Low | B66 |
NVFP4 | 4 | 3.9 GB | Medium | B66 |
Q4_K_M | 4 | 4.3 GB | Medium | B65 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | B65 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run Mistral 7B Instruct v0.3 on your machine.
Run
lms load Mistral-7B-Instruct-v0.3 && lms server startアップグレードオプション
Mistral 7B Instruct v0.3を快適に動かすハードウェア
RTX 3060 12GBコスパ重視RTX 5060 Ti 16GBコスパ最良16 GB VRAM (+8)448 GB/s (+192)
B
Raises estimated decode speed by about 84%.69.9 tok/sデコード
Raises estimated decode speed by about 84%.
Adds memory headroom for longer context windows and future model growth.
〜$449 MSRP
RTX 4060 Ti 16GBNVIDIAアップグレード16 GB VRAM (+8)288 GB/s (+32)
B
Raises estimated decode speed by about 28%.48.7 tok/sデコード
Raises estimated decode speed by about 28%.
Adds memory headroom for longer context windows and future model growth.
〜$499 MSRP
Frequently asked questions
Can GTX 1070 8GB run Mistral 7B Instruct v0.3?
Yes, GTX 1070 8GB can run Mistral 7B Instruct v0.3 with a B grade (Runs with offload). Expected decode speed: 38.0 tok/s.
How much VRAM does Mistral 7B Instruct v0.3 need?
Mistral 7B Instruct v0.3 (7B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.
What is the best quantization for Mistral 7B Instruct v0.3?
The recommended quantization for Mistral 7B Instruct v0.3 is Q4_K_M, which balances quality and memory efficiency.
What speed will Mistral 7B Instruct v0.3 run at on GTX 1070 8GB?
On GTX 1070 8GB, Mistral 7B Instruct v0.3 achieves approximately 38.0 tokens per second decode speed with a time-to-first-token of 5091ms using Q4_K_M quantization.
Can GTX 1070 8GB run Mistral 7B Instruct v0.3 for coding?
For coding workloads, Mistral 7B Instruct v0.3 on GTX 1070 8GB receives a B grade with 38.0 tok/s and 8K context.
What context window can Mistral 7B Instruct v0.3 use on GTX 1070 8GB?
On GTX 1070 8GB, Mistral 7B Instruct v0.3 can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
What should I upgrade first if Mistral 7B Instruct v0.3 feels slow on GTX 1070 8GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mistral-7b-instruct-v0.3-on-gtx-1070-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: